A Comparison of Deep Reinforcement Learning Algorithms
Submission For:
- Course: CMPT 888 Computer Animation
- Instructor: Dr. KangKang Yin
- Term: Spring 2018
- University: Simon Fraser University
Submission By:
- Name: Anmol Sharma
- Email: asa224@sfu.ca
- University: Simon Fraser University
Instructions
Folder Structure
logscontains log files generated during of various DRL agentsmodulescontains custom definitions of DRL agentsnotebookscontains practice/prototype notebooks for rapid experimentation and prototypingweightscontains saved pre-trained model weights corresponding to each agent and their experiments
How to Run (Individual Agents Training/Testing)
The file main.py is capable of loading, training and testing all the agents defined inside the custom_agents file in modules.
For example, if you want to train a DDPG agent on MountainCarContinuous-v0 environment from OpenAI Gym for 1000 episodes while visualizing the training process, you'd write the following:
python main.py --gym_id MountainCarContinuous-v0 --agent DDPG --episodes 1000 --exp exp_test
To test a pre-trained policy on an environment:
python main.py --gym_id MountainCarContinuous-v0 --agent DDPG --load ./weights/DDPG/<exp_name>/
How to Run (Reproduce Experiments in Report)
The experiments are divided into three task:
| Experiment Name | Environment | #Episodes |
|---|---|---|
| exp_1 | HalfCheetah-v2 | 15000 |
| exp_2 | Walker2d-v2 | 15000 |
| exp_3 | Hopper-v2 | 15000 |
Three shell scripts by the names:
PPO_Experiments.sh
TRPO_Experiments.sh
VPG_Experiments.sh
are included in the folder. Running these scripts will run the three experiments corresponding to each agent and each supported environment according to the table above.